Tag: Design Patterns

There are many skills that go into making working Intelligent Systems. As an analogy, in software you have base skills like:

Programming languages

Algorithms and data structures

Networking and other specialized skills

But then you have to take these skills and combine them to make a working system. And the ability to do this combination is a skill in its own right, sometimes called Software Engineering. To be good at software engineering you need to know about architecture, software lifecycles, management and program management — all different ways to organize the parts of the system and the people building the system to achieve success.

Software engineering skills are critical to moving beyond building small systems, with a couple of people, and to start having big impact.

When working with AI and machine learning you have to add a bunch of things to the base skills, including:

Statistics

Data science

Machine learning algorithms

And then maybe some specialized things like computer vision or natural language understanding

But then you also need to integrate these skills into your broader software engineering process, so that you can turn data into value at large scale.

And the ability to do this combination is a skill in its own right too. Not Software Engineering exactly, call it Machine Learning Engineering.

And here are two very important concepts in setting up an Intelligent System for success in practice:

The first is Closing the Loop between users and intelligence so that they support each other.

The second is Balancing the key components of your system, and maintaining that balance as your problem and your users evolve over time.

Taken together these form the basis of what I call the closed loop intelligent system pattern for applying machine learning.

Closing the Loop

Closing the loop is about creating a virtuous cycle between the intelligence of a system and the usage of the system. As the intelligence gets better, users get more benefit from the system (and presumably use it more) and as more users use the system, they generate more data to make the intelligence better.

So, for example in a search engine, you type your query and get some answers. If you find a useful web page, you click it and are happy. Maybe you come back and use the search engine again. Maybe you tell your friends and they start using the search engine. As a user, you are getting value from the interaction. Great.

But the search engine is getting value from the interaction too. Because when you click your answers, the search engine gets to see which pages get clicked in response to which queries. Maybe the most popular answer to a particular query is 5th on the list. The search engine will see that users prefer the 5th answer to the answer it thought was best. The search engine can use this to adapt and improve. And the more users use the system, the more opportunities there are to improve.

This is a virtuous cycle between the intelligence of the system and the usage of the system. Closing the loop between users and intelligence is key to being efficient and scalable with Intelligent Systems.

Doing extra work to close the loop, and let your users help your Intelligent System grow, can be very efficient, and enable all sorts of systems that would be prohibitively expensive to build any other way.

Balancing Intelligent Systems

There are five things you need to keep in balance to have a successful Intelligent System.

The Objective. An Intelligent System must have a reason for being, one that is meaningful to users and accomplishes your goals. The objective should be one that requires an intelligent system (and that you can’t solve easier and cheaper some other way), and it must also be achievable by the Intelligent System you will be able to build and run. Your objective might be relatively easy, or it might be hard, getting the objective right is critical for achieving success, and it is hard to do.

The Experience. An Intelligent System needs a user experience that takes the output of the intelligence (such as the predictions its machine learning makes) and presents it to users to achieve objectives. To do this the experience must put the intelligence in a position to shine when it is right—while minimizing the cost of mistakes it makes when it is wrong. The experience must not irritate users, and it must leave them feeling they are getting a good deal. And it must also elicit both implicit and explicit feedback from users to close the loop and help the system improve its intelligence over time.

The Implementation. The Intelligent System implementation includes everything it takes to execute intelligence. This involves things like deciding where the intelligence lives: in a client, a service or a backend. It involves building the pipes to move new intelligence to where it needs to be safely and cheaply. It involves controls on how and when the intelligence is exposed to users. And controlling what and how much to collect in telemetry to balance costs while improving over time.

The Intelligence. Most Intelligent Systems will have complex intelligences made up of many, many models and hand-crafted rules. The process of creating these can be quite complex too, involving many people working over many years. Intelligence creation must be organized so that the right types of intelligence address the right parts of the problem, and so it can be effectively created by a team of people over an extended time.

The Orchestration. Things change, and all the elements of an Intelligent System must be kept in balance to achieve its objectives. This orchestration includes keeping the experience in sync with the quality of the intelligence as it evolves, deciding what telemetry to gather to track down and eliminate problems, and how much money to spend building and deploying new intelligence. It also involves dealing with mistakes, controlling risk, and defusing abuse.